一、下载PCL1.9.1
Github下载地址：https://github.com/PointCloudLibrary/pcl/releases
下载红框内的两个文件
Github下载速度看人品。
二、安装
2.1 安装“PCL-1.9.1-AllInOne-msvc2017-win64.exe”。
（1）选择第二个，自动添加系统变量
（2）安装路径选择D盘，系统会自动新建PCL 1.9.1文件夹。
2.2 安装过程中需要安装OpenNI，选择路径（D:\PCL 1.9.1\3rdParty\OpenNI2）安装即可。
2.3 全部安装完成后，将pcl-1.9.1-pdb-msvc2017-win64.zip解压后的.pdb文件拷贝到（D:\PCL 1.9.1\bin）中。
2.4 设置环境变量：右击计算机—属性—高级系统设置—高级—环境变量—用户变量—Path—编辑!
如下图所示，设置完成后重启电脑。
在这里直接给出，防止出现错误（依次添加）：
%PCL_ROOT%\3rdParty\FLANN\bin
%PCL_ROOT%\3rdParty\VTK\bin
%OPENNI2_REDIST64%
%OPENNI2_LIB64%
%OPENNI2_INCLUDE64%
到此，环境变量的配置完成。
三、配置
3.1 新建空项目，
解决方案配置选择Debug，解决方案平台选择x64。
3.2 右击工程—属性—VC++目录—包含目录，添加7个include路径
具体添加的include路径如下：
D:\PCL 1.9.1\include\pcl-1.9
D:\PCL 1.9.1\3rdParty\Boost\include\boost-1_68
D:\PCL 1.9.1\3rdParty\Eigen\eigen3
D:\PCL 1.9.1\3rdParty\FLANN\include
D:\PCL 1.9.1\3rdParty\Qhull\include
D:\PCL 1.9.1\3rdParty\VTK\include\vtk-8.1
D:\PCL 1.9.1\3rdParty\OpenNI2\Include
3.3 VC++目录—包含目录，添加6个lib路径
具体添加的lib路径如下：
D:\PCL 1.9.1\lib
D:\PCL 1.9.1\3rdParty\Boost\lib
D:\PCL 1.9.1\3rdParty\FLANN\lib
D:\PCL 1.9.1\3rdParty\Qhull\lib
D:\PCL 1.9.1\3rdParty\OpenNI2\Lib
D:\PCL 1.9.1\3rdParty\VTK\lib
3.4 C/C++—常规—SDL检查—否
此处可能会出现找不到 **C/C++** 选项的情况，解决方案为：
新建一个C++源文件之后，再打开属性页就可用看到C/C++选项。
3.5 C/C++—预处理器—预处理器定义—添加：
_CRT_SECURE_NO_WARNINGS
_SCL_SECURE_NO_WARNINGS
_SILENCE_FPOS_SEEKPOS_DEPRECATION_WARNING
3.6 配置属性—调试—环境—添加：
PATH=D:\PCL1.9.1\\bin;D:\PCL1.9.1\\3rdParty\FLANN\bin;D:\PCL1.9.1\\3rdParty\VTK\bin;D:\PCL 1.9.1\\3rdParty\OpenNI2\Tools
3.7 链接器—输入—附加依赖项——添加PCL和VTK的相关lib文件。我用的Debug版本。
附加依赖项具体添加内容如下：
输入到属性表里边的时候必须一行对应一个lib才能成功。
Debug版本
pcl_common_debug.lib
pcl_features_debug.lib
pcl_filters_debug.lib
pcl_io_debug.lib
pcl_io_ply_release.lib
pcl_kdtree_debug.lib
pcl_keypoints_debug.lib
pcl_ml_debug.lib
pcl_octree_debug.lib
pcl_outofcore_debug.lib
pcl_people_debug.lib
pcl_recognition_debug.lib
pcl_registration_debug.lib
pcl_sample_consensus_debug.lib
pcl_search_debug.lib
pcl_segmentation_debug.lib
pcl_stereo_debug.lib
pcl_surface_debug.lib
pcl_tracking_debug.lib
pcl_visualization_debug.lib
vtkalglib-8.1-gd.lib
vtkChartsCore-8.1-gd.lib
vtkCommonColor-8.1-gd.lib
vtkCommonComputationalGeometry-8.1-gd.lib
vtkCommonCore-8.1-gd.lib
vtkCommonDataModel-8.1-gd.lib
vtkCommonExecutionModel-8.1-gd.lib
vtkCommonMath-8.1-gd.lib
vtkCommonMisc-8.1-gd.lib
vtkCommonSystem-8.1-gd.lib
vtkCommonTransforms-8.1-gd.lib
vtkDICOMParser-8.1-gd.lib
vtkDomainsChemistry-8.1-gd.lib
vtkexoIIc-8.1-gd.lib
vtkexpat-8.1-gd.lib
vtkFiltersAMR-8.1-gd.lib
vtkFiltersCore-8.1-gd.lib
vtkFiltersExtraction-8.1-gd.lib
vtkFiltersFlowPaths-8.1-gd.lib
vtkFiltersGeneral-8.1-gd.lib
vtkFiltersGeneric-8.1-gd.lib
vtkFiltersGeometry-8.1-gd.lib
vtkFiltersHybrid-8.1-gd.lib
vtkFiltersHyperTree-8.1-gd.lib
vtkFiltersImaging-8.1-gd.lib
vtkFiltersModeling-8.1-gd.lib
vtkFiltersParallel-8.1-gd.lib
vtkFiltersParallelImaging-8.1-gd.lib
vtkFiltersPoints-8.1-gd.lib
vtkFiltersProgrammable-8.1-gd.lib
vtkFiltersSelection-8.1-gd.lib
vtkFiltersSMP-8.1-gd.lib
vtkFiltersSources-8.1-gd.lib
vtkFiltersStatistics-8.1-gd.lib
vtkFiltersTexture-8.1-gd.lib
vtkFiltersTopology-8.1-gd.lib
vtkFiltersVerdict-8.1-gd.lib
vtkfreetype-8.1-gd.lib
vtkGeovisCore-8.1-gd.lib
vtkgl2ps-8.1-gd.lib
vtkhdf5-8.1-gd.lib
vtkhdf5_hl-8.1-gd.lib
vtkImagingColor-8.1-gd.lib
vtkImagingCore-8.1-gd.lib
vtkImagingFourier-8.1-gd.lib
vtkImagingGeneral-8.1-gd.lib
vtkImagingHybrid-8.1-gd.lib
vtkImagingMath-8.1-gd.lib
vtkImagingMorphological-8.1-gd.lib
vtkImagingSources-8.1-gd.lib
vtkImagingStatistics-8.1-gd.lib
vtkImagingStencil-8.1-gd.lib
vtkInfovisCore-8.1-gd.lib
vtkInfovisLayout-8.1-gd.lib
vtkInteractionImage-8.1-gd.lib
vtkInteractionStyle-8.1-gd.lib
vtkInteractionWidgets-8.1-gd.lib
vtkIOAMR-8.1-gd.lib
vtkIOCore-8.1-gd.lib
vtkIOEnSight-8.1-gd.lib
vtkIOExodus-8.1-gd.lib
vtkIOExport-8.1-gd.lib
vtkIOExportOpenGL-8.1-gd.lib
vtkIOGeometry-8.1-gd.lib
vtkIOImage-8.1-gd.lib
vtkIOImport-8.1-gd.lib
vtkIOInfovis-8.1-gd.lib
vtkIOLegacy-8.1-gd.lib
vtkIOLSDyna-8.1-gd.lib
vtkIOMINC-8.1-gd.lib
vtkIOMovie-8.1-gd.lib
vtkIONetCDF-8.1-gd.lib
vtkIOParallel-8.1-gd.lib
vtkIOParallelXML-8.1-gd.lib
vtkIOPLY-8.1-gd.lib
vtkIOSQL-8.1-gd.lib
vtkIOTecplotTable-8.1-gd.lib
vtkIOVideo-8.1-gd.lib
vtkIOXML-8.1-gd.lib
vtkIOXMLParser-8.1-gd.lib
vtkjpeg-8.1-gd.lib
vtkjsoncpp-8.1-gd.lib
vtklibharu-8.1-gd.lib
vtklibxml2-8.1-gd.lib
vtklz4-8.1-gd.lib
vtkmetaio-8.1-gd.lib
vtkNetCDF-8.1-gd.lib
vtknetcdfcpp-8.1-gd.lib
vtkoggtheora-8.1-gd.lib
vtkParallelCore-8.1-gd.lib
vtkpng-8.1-gd.lib
vtkproj4-8.1-gd.lib
vtkRenderingAnnotation-8.1-gd.lib
vtkRenderingContext2D-8.1-gd.lib
vtkRenderingContextOpenGL-8.1-gd.lib
vtkRenderingCore-8.1-gd.lib
vtkRenderingFreeType-8.1-gd.lib
vtkRenderingGL2PS-8.1-gd.lib
vtkRenderingImage-8.1-gd.lib
vtkRenderingLabel-8.1-gd.lib
vtkRenderingLIC-8.1-gd.lib
vtkRenderingLOD-8.1-gd.lib
vtkRenderingOpenGL-8.1-gd.lib
vtkRenderingVolume-8.1-gd.lib
vtkRenderingVolumeOpenGL-8.1-gd.lib
vtksqlite-8.1-gd.lib
vtksys-8.1-gd.lib
vtktiff-8.1-gd.lib
vtkverdict-8.1-gd.lib
vtkViewsContext2D-8.1-gd.lib
vtkViewsCore-8.1-gd.lib
vtkViewsInfovis-8.1-gd.lib
vtkzlib-8.1-gd.lib
四、测试
示例代码
#include <iostream>
#include <vector>
#include <ctime>
#include <pcl/point_cloud.h>
#include <pcl/octree/octree.h>
#include <pcl/visualization/pcl_visualizer.h>
using namespace std;
int
main(int argc, char**argv)
{
srand((unsigned int)time(NULL));
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
// 创建点云数据
cloud->width = 1000;
cloud->height = 1;
cloud->points.resize(cloud->width * cloud->height);
for (size_t i = 0; i < cloud->points.size(); ++i)
{
cloud->points[i].x = 1024.0f* rand() / (RAND_MAX + 1.0f);
cloud->points[i].y = 1024.0f* rand() / (RAND_MAX + 1.0f);
cloud->points[i].z = 1024.0f* rand() / (RAND_MAX + 1.0f);
}
float resolution = 128.0f;
pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree(resolution);
octree.setInputCloud(cloud);
octree.addPointsFromInputCloud();
pcl::PointXYZ searchPoint;
searchPoint.x = 1024.0f* rand() / (RAND_MAX + 1.0f);
searchPoint.y = 1024.0f* rand() / (RAND_MAX + 1.0f);
searchPoint.z = 1024.0f* rand() / (RAND_MAX + 1.0f);
// 体素内近邻搜索
vector<int>pointIdxVec;
if (octree.voxelSearch(searchPoint, pointIdxVec))
{
cout << "Neighbors within voxel search at (" << searchPoint.x
<< " " << searchPoint.y << " " << searchPoint.z << ")" << endl;
for (size_t i = 0; i < pointIdxVec.size(); ++i)
std::cout << " " << cloud->points[pointIdxVec[i]].x
<< " " << cloud->points[pointIdxVec[i]].y
<< " " << cloud->points[pointIdxVec[i]].z << std::endl;
}
//K近邻搜索
int K = 10;
vector<int>pointIdxNKNSearch;
vector<float>pointNKNSquaredDistance;
cout << "K nearest neighbor search at (" << searchPoint.x
<< " " << searchPoint.y << " " << searchPoint.z << ") with K=" << K << endl;
if (octree.nearestKSearch(searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
{
for (size_t i = 0; i < pointIdxNKNSearch.size(); ++i)
cout << " " << cloud->points[pointIdxNKNSearch[i]].x << " "
<< cloud->points[pointIdxNKNSearch[i]].y
<< " " << cloud->points[pointIdxNKNSearch[i]].z
<< " (squared distance: " << pointNKNSquaredDistance[i] << ")" << endl;
}
//半径内近邻搜索
vector<int>pointIdxRadiusSearch;
vector<float>pointRadiusSquaredDistance;
float radius = 256.0f* rand() / (RAND_MAX + 1.0f);
cout << "Neighbors within radius search at (" << searchPoint.x
<< " " << searchPoint.y
<< " " << searchPoint.z
<< ") with radius=" << radius << endl;
if (octree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
{
for (size_t i = 0; i < pointIdxRadiusSearch.size(); ++i)
cout << " " << cloud->points[pointIdxRadiusSearch[i]].x
<< " " << cloud->points[pointIdxRadiusSearch[i]].y
<< " " << cloud->points[pointIdxRadiusSearch[i]].z
<< " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << endl;
}
// 初始化点云可视化对象
boost::shared_ptr<pcl::visualization::PCLVisualizer>viewer(new pcl::visualization::PCLVisualizer("显示点云"));
viewer->setBackgroundColor(0, 0, 0); //设置背景颜色为黑色
// 对点云着色可视化 (red).
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>target_color(cloud, 255, 0, 0);
viewer->addPointCloud<pcl::PointXYZ>(cloud, target_color, "target cloud");
viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "target cloud");
// 启动可视化
//viewer->addCoordinateSystem(0.1); //显示XYZ指示轴
//viewer->initCameraParameters(); //初始化摄像头参数
// 等待直到可视化窗口关闭
while (!viewer->wasStopped())
{
viewer->spinOnce(100);
boost::this_thread::sleep(boost::posix_time::microseconds(1000));
}
return (0);
}
五、可能报错及解决方案
1、添加头文件``#include <pcl/registration/icp.h>``编译的时候可能会遇到如下报错：`1>...\flann\algorithms\dist.h(523): error C3861: “pop_t”: 找不到标识符`
解决方法：
双击该行输出直接打开`dist.h`头文件，将第503行的`typedef unsigned long long pop_t;`移动到第480行前面（如下图）；
2、可能出现如下情况：
解决方法：
3、其他错误情况目前没有
五、结果
会出现如下结果，输出的是渲染信息，关掉即可，没什么用。
至此，PCL1.9.1配置完成!!!!!该版本的PCL点云库兼容VisualStudio2019，配置方法完全相同。
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